very much for the introduction

uh

we talk about anomaly detection

which is a topic which is being around one time

uh the reason why i'm interested in this topic is that the

so we have a national

project

the major object

which it is addressing the issues both you based on the computer vision system

other

the main application slight changes

what do you do you have to start from scratch

all you have to can you use some of the models and uh one of

the issues that one

in this context

it's uh on the detection because

the system has no

that if it is fully automatic system

because you know that the it cannot cope with

the main in both uh that uh because no competence to in that the

since the data so that's the context and because it's a reasonably project

we in the groove in psychology of the community college london

so the plan is the

stop

the background then we want to on the money detection

uh

we review all uh right out on anybody detection and that

a little bit of it is

all

approaches

and that will then be all position

yeah

solely on the money detection

section system channel

and the

we apply

oh set

the problem

you know

interpretation system

so that's plan to

so if you

this on vision system we present system the difficult to a stage is and the

first of all the to the remote modules

solving lost six

do about the if you are not just like to do not basically problem but

uh

image processing vision i want to see you developing a system that actually application and

many other issue

think about the channel

you need to collect a lot of training data because the existing systems uh i

let me

observations

we do not know what is

indicating that

and the optimized system so it's like that

uh nobles that's the goal go through an image that is why convolving

and just uh as an example are we talking about the tennis video analysis

you

for some

school

yeah so uh and that was just a very few men version of this is

that the linear

and uh so it's at a G is to you

an application and then

all services about i

okay so um

the conference here is the uh is concerned with advanced concepts and in a way

when you develop uh and interpretation system then uh in the sense that system is

advanced in its own right so i could be just talking about the video uh

the tennis video notation system but then my focus will be more on the second

body point

as i already mentioned so suppose you want to add up the system to some

other domain uh even quite close domain and go see that the applications i will

be uh talking about a very simple indeed nevertheless uh raising like you interest in

issues and challenges

and that if you want to go that if you want to

benefit from many years of after and then try to use what you have and

to develop a new uh competence you capability than possible you have to

identify that you have a problem that you cannot cope with some input and uh

then you have to modify the system inappropriate way and there are of course the

other communities at all this stuff community support computer vision that whether or not a

transfer that i mean and uh so uh

will not be addressing those issues but uh at the end that once you have

adopted the system and some new application then

when i say i'd update

i really mean develop new capability then the system needs yeah and not the functionality

it needs to know uh can make sure a situation it is operating and that

should be able to classify the context and uh in which it operates so that

it can automatically select the appropriate uh domain knowledge voice separation so

this is the system that we developed so basically it's the can analyze tennis video

the way uh

that we describe what the system looks like by the in principle

the objective is that uh from the video it input completely automatically you are able

to interpret what's going on to the point of points awarded avoiding the uh generating

school from the process now

i'm not talking about the uh style whole uh yeah we develop a system which

works that from two D standard the real cost video okay so that makes a

problem it would be difficult but anyway so in principle when you break the video

into shots you want to know what's happening in short

well as or so seconds uh and that there is not only uh who actually

means in the running and we should be awarded a point

no

probably unless you are young and have very good a nice and uh you will

not be able to see the detail about the this is just to illustrate the

complexity of the system

and that it has uh why the few levels of course in so initially the

uh video is broken into shorts and then the short each shot this process the

separately basically uh and that is the

level processing deals with the foreground-background separation

then the key components of the content are extracted which is the motion of the

ball and the players and the then the system yeah that uh means uh important

events

and which is uh one important event is when the board changes detection and way

it changes direction

and then eventually there is some high level interpretation process of these talents so this

is a more digestible somebody of the system okay about that basically the ball tracking

is the most important you need to know whether code is uh you need to

the text is important events and there is a high level interpretation part which is

basically hidden markov model based

no most of the modules that the system has use context in some way okay

so when i talk about context here it's not the context it's not the domain

where the system operate but it's the local context which is like the temporal or

spatial so when you want to interpret for instance uh what's going on need to

know not only whether board is but also whether players are so uh that is

the interaction between objects in the video uh so in principle you are interested in

integrity in every object in each frame about the neighboring objects have a uh

one may also information which is which is very important and you want to use

this to uh information jointly uh they provide contextual information and you want to use

this information jointly to make interpretation so in principle you have some slow but knowledge

domain knowledge which is a quite in some way i the through line in or

partly through

yeah so you didn't in the prior knowledge in uh and you are then comparing

observations ritual model to make interpretation so this is very genetic uh indication that most

of the modules are dealing with contextual information many more usability contextual information uh over

time okay so that uh about the other modules deal with the spatial contextual information

and some of them with both

so the first one for instance is a module which is uh separating foreground and

from background so you may want to what happened here uh

because players disappear but basically it's the module which is below the remote site so

you take video frames from a shot and the and relate them to each other

and uh basically that allows you to go to was i and anything that's movie

that frame is wiped out because it uh not the assistant information and so you

have basically a background and then you can use the background to separate the foreground

probably so

that's one example all the that all this type of functionalities that the modules perform

the most important one once you have uh

uh the players and the can extractable used to detect the events so you can

see that the so it's the ball tracking problem uh process and that each is

also detecting when the ball is changing detection and uh you know uh where the

code is that has been automatically the big picture it's a fully automatic system we

can uh and that you also can detect players and from that you can derive

interpretation

this is uh

so these are the events that we have extracted in time and the

the sequence of these events and the position but they happen any action or more

advanced a bit plane is a determine what's going on and you have a hidden

markov model but it's a lot of the temporal structure in a small gains in

general and so the mean pennies uh which allows you to interpret what's going on

and you can then decide to who should be awarded to point at the end

okay so and this is an example of what the system would produce so he

on the left hand side you to actually tell you what's going on was awarded

the point at one time at a tool training

okay so we as i said you spent three years developing the system and we

were just working with one video and it happened to be a video singles

and then a somebody else question about what would happen if you actually applied it

to doubles and you know so it's very simple the small transition but the nevertheless

at uh

significant enough transition for the system to fail so uh and uh

so that's one thing about the

it's not only question all system fail in you also would like to know uh

when it fails to white fellows and can use land or something from it

anyway so the question is what are the mechanisms that are needed for the system

i didn't to realise that it's actually no longer competent to perform a certain functionality

and the how can this functionality be extended

already mentioned so this is the project the that we have features been sort of

a motivating the work in this area and the anyway so already i think alluded

to these mechanisms that we need to i don't to take this we need to

cross knowledge and the we need the to adapt interpretation processes and acquire new competencies

that way

okay so

these are the mechanism this is done is and to what i'm going to focus

on anomaly detection so already talked with twenty minutes and i haven't the restarting the

topic of the of the lecture okay so uh these are the mechanism that would

be normally needed and that but one of the nice anomaly detection

oh if you look at

the

it well as the definition of on the money to start with and it's a

normally understood this um so something deviating from automatically but the that the how the

normal it is defined yeah is very general and that can be some sort order

it can be sort of a statistical normally you can be a rule whatever so

it's uh original there are also many synonyms and the interestingly some of these uh

pseudonames the general mean

deviation from normality about the sometimes the uh they have some uh additional nuance uh

and that they may need for in cincinnati

yeah regularity okay innovation so there is a

difference between uh and the money and innovation because innovation usually means implies a change

is of constant change you moving to some of the uh model of a proxy

experience

now what is that conventional model i think everybody knows that the menu look what

anomalies you are normally thinking in terms of uh outliers of some distribution uh so

you have a gaussian for instance and that was the

uh making observations away yeah then used several it must be applied must be anomalous

observations because it's not pretty consistent with my model of the data the experience the

time uh that i make the past so one is a

look in uh and basically the mathematical model is a statistical one in principle and

the uh

sometimes you the not only work with a single observation but the weight the multiple

observations and then you may be interested whether uh we distribution of the all observations

are different from the distributions of but uh of your model and uh so you

could also be talking about the sum so that uh normally in terms of the

shape of the distribution

as i said to anomaly detection has been of interest for a long time uh

domain and value goes back to the nineteenth century a people have been interested in

developing normal model so gaussian models and uh for model in various uh sets of

data observations and the and how they have been detected by the model is uh

when the observation is consistent with that model so over the uh hundred years i

suppose most of the work has been focusing on this type of concept of but

uh no money and there are excellent surveys which uh make like quite easy and

uh recently quite a lot of working in on the money detection comes from the

security and the surveillance the communities as they are very much interested in formulating the

problem of but uh detecting the something unusual as the and on the water detection

problem but that although they may be using quite complex system most of the uh

notions of on the money in these the papers are very close to the statistical

notion so even if you have a complex just images multiple layers of interpretation very

often people still uh loop on the money from these the uh from these models

so you can estimate are presented in a very simple way is here so this

is your basic system which is performing sometimes you have sense uh you got some

usually single hypothesis model

uh so i could distribution and the there and uh this derive some action something

that something and you are interested to know whether the uh that is any and

all money so you need some sort of a anomaly detector and usually would be

some sort out lie detector and if it is an outlier then hopefully it will

affect the action so you will not but for what you would normally performed

no in a complex systems like uh a video system tennis video system you need

to model like this big every model okay

many of these modules are dealing with the multiclass problems so you don't have just

a single

hypothesis you have multiple hypothesis which is also introduced in the interest in complexity the

into the equation you have a

many levels of course in and some of these models are delay in a weighted

high level information they have that down uh using contextual information and uh so although

they may be interpreted the same sort of a have and they will be using

different sources of information and so all these uh complexities are somehow not cultivate indicated

weighted by these dimensional anomaly detection uh model so already mentioned so this the list

of things so we have multiple models not just a single white with two hypotheses

model

importantly in a much in perception

very often we use discriminative approaches rather than generically if using discriminative approach you cannot

really talk about outliers because you just know whether things on the right side of

the boundary on all but the you have completely lose the uh every idea of

that the observation which the which are trying to classify as an outlier on all

is lost the uh to the system so um and if you wanted to detect

a normally

you would need to use both discriminative models get better performance but also maintain a

generative model to know what's going on whether you are actually competent to make that

decision

uh you have very often areas in the observation space where you have a genuine

ambiguity now give a genuine on but then the decisions you make you make in

uh you have to be very careful about the menu can not necessarily interpret them

as kind of money because you are you have a ambiguous situation you cannot have

confidence that it's going to be an anomalous observation

contextual reasoning already mentioned that the uh

existing systems are not ready yet to deal with that and hierarchical representation

about the two more things uh data quality you need to know whether the observation

data you wanted and weighted is of the same quality as the data with the

page the system has been designed you know that you make certain assumptions about the

quality of the data any that quality changes then

you the system has to decide if you differentiate between that situation and uh because

it would be starting making errors okay and the anomalous situation where you if you

have good quality data can be pretty confident that if something is the image then

that it's going to be anonymous so the observation

and uh

more the boolean because it's a very often one

introduced is uh

a potential one another situation

by uh

you'll interpretation process because you want make that process to be as fast as possible

so for instance if i am interested in object recognition and i know there is

uh i don't know half a million objects

right at hundred thousand objects you look at the various names and dictionary whatever it

would be completely foolish to have a system which can interpret and very single object

from that hundred thousand one place so you would the room that leads to something

manageable and hopefully we'll deal we just uh i don't have it and the hypothesis

on the list and all than a hundred thousand and that if you do that

then you may observe something which is an autonomous but by your decision because you

have actually simply by the system goes uh processing strategy is and making the assumption

that the object will come only from this subset you yeah and if it doesn't

then you should be able to detect it and recognise it and to do something

about so you can then inject more hypotheses into the system uh if the none

of the existing hypotheses is uh to get

so

i talked about the deficiencies of or not normal anomaly concepts and just to show

you more examples of the different nature all but not on the model situation so

very often

one is ask uh to solve the problem of spotting the difference okay so you

can consider it also as a on the money detection problem so in this particular

situation we have a nice a nice little object and that i think everybody cans

for the difference is a head of a cat hopefully or something uh in the

second picture are there any other animals

very good yeah

uh so this object has slightly different like uh angle any other

yeah and the little bit shifted very good so we are very good on the

money detectors

but the uh the first instance was not all that will be is that all

these uh the other animal is represent about the you know very simple uh comparison

uh and four that's a computer systems are extremely good uh able to detect uh

the dependencies and the you can uh in well okay so that's uh that's one

example you have we already talked about distribution drape you talked about mobile the innovations

anyway what about the this case

are there any other monies

well actually there are no differences the only difference is for maybe actually what to

observe an image of a very acute vision uh what you jobs uh is the

difference in uh information about the second image has been compressed data okay so you

lose a little bit of a high frequency information but uh so obviously the compression

introduces an obvious and if i have a on the money system which is to

detect independence is that based on the sums of assume distribution and uh suddenly the

noise characteristic change then uh you know is that difference not so this should not

be detected as a normal is so big that quality is an extremely important concept

in the in the process

already talked about the

uh contextual information and the or and hierarchical representation speech also exploit contextual information and

uh so you know here

every object in this image which is famous painting uh

make sense is able to find about the relationship of these objects is the obviously

unusual because you would not expect the locomotive to be jumping out of the fireplace

and the uh so

uh it's another example of the type of anomaly that you would like to be

able to detect and

explored and the system should be exploited so this is the conventional system that uh

people have been using them almost four hundred years and um

and this is probably what we need okay so

the difference between that well this is the actual functioning system which is uh implement

in some applications uh this just uh is the same thing is the blue box

which has sensor and the actions alignment

when ten okay

the difference between this and that is that we have a probably multiple hypotheses of

hypotheses the for each uh module okay and the or so we have probably several

layers of interpretation not just a single layer we sure uh

yeah so the high less would be using context and uh so that is the

relationship between those players uh so you then need if you want to the text

on the money in a sensible way you then need the following you need something

that deals with the differences between contextual or non contextual processing

and that that's a soap incongruence detector okay so uh yeah which is so if

you have an object if i go uh back to my

good really uh if i go here

if i and this is my scene graphs or something estimation and in principle i'm

uh trying to interpret every object okay but we know that i am interpreting one

object uh in the to get off then i'm used in the contextual information provided

by other objects so in principle you can uh you are interpreting that object in

two different ways possible just using the measurement information relating to that object

and secondly you use the measurement information and possibly prior knowledge about the configuration of

one or contextual information provided by the neighbours which are will have impact on the

interpretation of the subject so we have soft contextual and non contextual

in the presentation and you can be measured in then continuance between those two

uh

but we need to other things

we need to assess battle or do not actual one and for the contextual one

uh whether we have any but we are dealing with ambiguity so what how much

confidence we actually have in the interpretation that we are making so that's a one

of the things that the needs to be i did in addition to incongruent uh

we need to a module which is a seen data for the because that module

tells us whether we really should be

looking for a normally sober that even if you'd the text something spurious uh whether

we should consider it as a normally because if the data quality has changed then

we should not be uh

simply saying well it's anomalous situation because so uh yeah the

incorrect decisions so what about the change that will be induced by uh data of

different quality uh well we should be you know and the

and in addition to all that we need to the east and that

uh anomaly detection process is the outlier detection process is because even if my non

contextual and contextual decision making process is a uh

functioning well and uh to function well they would be probably based on the stigma

not body models then i will need

some way of method deciding whether the observations a on the models are not whether

they are outliers so i still need to the conventional model okay of undermining so

that can see that these two blocks are the cable uh non contextual and contextual

process

but hopefully i will not be using them very often because if i did lana

the system would just the be computationally complex so uh

ideally what uh you would like to do is to

bros processing in these modules looking for our model is only when you want to

get to do so and this the to get in can be done quite efficiently

why this incongruence detection process

now

can see that one of the mechanisms and only one there are others uh in

uh

the system that we need for detecting a normally scene perception systems is uh incongruence

detect that and interestingly uh the work which uh well one of the original work

in this area uh was running speech area uh

i don't know whether actually brno was involved in this or more uh was it

was just one of yours

okay yeah so you work with the hynek hermansky and um work on the problem

all the out-of-vocabulary what detection which is exactly the sort of a big a typical

example of the problem we are dealing with you may have a uh you have

a at least player speed a system which is processing data uh detecting phonemes so

we have non contextual interpretation and contextual which combines the phonemes in words and you

may be interested in detecting and whether there is any anomaly and that would be

an or more like if for instance the phoneme detector functions that very well gives

you very strong confidence in the interpretation but uh the

word-level interpretation of police is garbage and it reduces got it's simply because the word

doesn't exist in the dictionary

so this is the no example of the situation uh that uh we would like

to detect and the there was a five year project direct project funded by the

U which is uh as being extending this basic idea to the image domain

and the and also continued with application in speech and uh so that was uh

but also by will get which it was then uh extending this work uh and

the most of the other work which are the definitely want role is uh

this name it yet the publications was published in the subsequent about two thousand and

i two thousand and well so

this is a little bit on the background about as i say is not directly

focus and finally on the incongruent so detection how do you uh the fact that

there is a difference between sort of a generic and the specific classifiers generally be

in uh non contextual one uh well depends on the application about the

and the if uh what is the implication of uh detecting such incongruence so that's

uh what dialogue has produced but maybe actually try to use this in a only

work on the tennis video interpretation it was not you know what the very citizen

fine mention be a very open dealing with situations where the decisions but ambiguous and

then you would not a bit on from that come from that you want but

and with a normal situation we dealt with situations and we'll see that in a

minute that the uh we had several videos of pennies and the

even several videos of any single they all had a different chord to the from

different tournaments so uh they had the uh the recorded in different conditions and uh

some of them but noisier than others and that it was pretty a that you

need to know something about data quality if you want uh to make a sensible

uh decisions about on the money we still need it the basically the original uh

technology so to speak of a normally detection so how by detection proces and uh

so i think these were or right and what do they monitoring also is needed

to measure whether distributions of shifted

no wit is uh

architectural system that is the state it it's a quite interesting because you can then

based on the various uh

uh

on the outcomes or on the analysis of the uh the various modules in that

anomaly detection system you can then a classifier you anomalies or situations yeah and they

recognise different states so we can definitely recognise the state when you have no anomaly

but you can also uh identify situations when you are dealing with an unknown up

with noisy measurements you can uh the text situation that you have unknown objects uh

when you have an incongruent or congruent labeling so all the various a space of

uh nobody can be detected and to you get much better idea of what's going

on

so ideally actually what we want to do is to start with ten days and

move on to badminton and uh do uh detector or identify with the modules that

will not have competence to well on the input data and uh try to correct

the module so i don't then all inject knowledge so that the we can actually

use the system volume application

but the

the wise you started something very simple and as i said just switching from singles

tennis doubles so very simple situation so if you consider that problem then

what would you expect

first of all

in doubled there are twice as many players

that's yeah but the cold that is being used for the game is a wider

so you have also the time lines which can uh

which are illegal basically in the case of singles about in the case of doubles

of uh they are more and the but everything else stays the same the rooms

are the same that was that was quite a nice the

uh

challenge because it was not too complicated about the at the same time why the

interesting to see what's going on and uh okay now in principle you would say

well it's obvious well can just count the players and the drop is done about

the impact is anybody who works and you or working in on images or video

you know that the tech T and count been objects it's not as simple as

that uh well lee because

the vision process is are not perfect but partly because the uh application domain allows

basically

uh

well this is not the use of a black and white so we speak about

the it's not either two or four in the game but the there are other

moving objects so you have line charges for instance and normally this tells us they

still and when you uh do the most i can then use of uh they

stay in the image about that sometimes they move okay and if they move they

suddenly become moving object and uh then unless you have some sophisticated mechanism of distinguishing

between players and other moving objects then you are stuck with the different count then

you have more balls okay so the se is played and it goes out and

the more boy runs collectible and uh so you have somebody five

object detected that so if you actually look at and the statistics of a video

okay uh not just the then uh this is what you would to the observed

for singles okay so most of the time you would the detect just to plan

to agents movie nations about the we in the many occasions uh you detect a

human on and uh sometimes up to five so we have a distribution and equally

for doubles uh you have a distribution so you have two sets of this the

uh

you look on the money on the basis of distributions rather than single observations but

anyway so we are basically trying to differentiate between uh

two distributions one which is a modal distribution and one which is of the distribution

and look for differences and that anyway so that's uh what we have a downer

which is a source standard approach and here we have some uh

not the results but the data that we use so we have can see we

have five videos uh of different length so they are not necessary or complete much

is about the white it doesn't that they all of a different situation so we

have uh australian uh japan tournament and us women and men single doubles and these

are the numbers of the place and um

and here we have some results okay so what we show here body to you

as we are comparing distributions if you are using an into information just from one

short then this will give you the performance that you would get

for various scenarios okay and the uh basically uh here we are talking about the

detection of under forty so uh we train on singles and when i talk about

a normally i'm or there's S you mean that any training that is done is

or was down in the norm a normal situation there are many cases where people

are actually trying to synthetic pretty uh genetic on the monies create animal is and

the uh but i think it's fundamentally wrong approach because that if you uh design

a system you cannot possibly collect data or a normal situation for the idea uh

and well then they would just becomes of new classes and the so the really

this they're the appropriate the way of thinking about it is that you cannot train

the system only with the norm on the most data and so order training was

done only on singles we measured the level of noise and you can see for

instance that the was thirty and uh men single pay that much lower high noise

then uh the other two and uh and that uh

uh

has a serious implication because if you look at the data

you can see that the if you train or no uh so here we have

information okay here we trained on the uh australian women singles and japan single okay

so you can see that the

if you train on the uh good quality data and then you try to uh

that's the system with the data of different quality then you have problems in you

can see that from this guitar because this is basically the unwanted detection output or

the single was so we should not be detecting any animal is because the art

doesn't dealing with the same domain the system was trained to but uh to recognise

the right interpret the tennis singles and here we are actually having a problem because

the course of the noise condition uh we are uh detecting force anomalies uh right

is that when we actually use the trained on data which is a little bit

more noisy than that

not all the best uh singles throws any animal is about the uh then we

have to do a little bit more integration to get actually the results uh the

unwanted direction di can correctly so that also shows you that the uh

one is to be very careful about data quality and you just implications on the

on the money detection process

uh the second to the task was to well the second on the money that

can analyze is that the ball goes out in the time lines and

okay and the U

so the gain should terminate

but it doesn't just got it on and uh

again we have developed a so what do we have well we use

uh had be very careful to make sure that the a normal role in us

on the models out who uh situations where uh which may genuinely ambiguous and because

of the data in on the system itself anything very close to the boundary line

between the timeline and the single school was on the models but the further away

you got from that the remote the from the boundary line you have more confidence

so we have values into this a confidence measure

we as a filter to make sure that we are not trying to make uh

decisions about on the money uh on data which is by its very nature i

don't the ambiguous

coming back again to my point that we are always using only the information that

you acquire obtain in the local a problem but uh normal source norm and the

model souls and uh so basically

and the interpretation and the interpretation process associated with it so we have not really

designed the system simply to detect the specifically on the money sits do in normal

processing and the uh detecting on the monies as a result of that and the

anyway

this is a just um an illustration of uh of the interpretation process in the

so when there is a perceived

uh there are okay well as when the system should that i mean a and

actually the game continues we are uh follow in all the possible interpretations uh all

the possible a interpretation possible it may happen and the uh on the basis of

a that we are able to make a decision whether uh there is a no

money because the game continues uh without uh bases and the two

the detection is based on measuring incongruence between

uh contextual a non contextual uh playgirl's basically so we have our event detection which

is uh give you know so non contextual labels and we have the context of

course in which takes into account the sequences uh of events over time so as

uh

as this are normally the case you have basically as i already explained you have

two interpretations one which is contextual non contextual and you have to measure whether they

are incongruent

and one possible way of measuring it is using solve a bayesian surprise measure which

is the form of a divergence on a discrete distributions of labels about the problem

with that the measure is that uh it's very sensitive if you have a uh

a probability which moves from point ninety five one then a suddenly you move into

infinity and it the course this the hubble and uh so we have actually adapted

that mention and to use the something which was a practically a much more efficient

so we chose the top label the most uh the best supporting label for each

of the contextual or non contextual hypotheses and just measure the difference between those two

and you can actually show that in the two class case that we consider in

this particular uh application whether the ball was out not uh we uh it ended

up with a very simple way of measuring an incongruence between the states and when

we did that on the videos that we trained with also we trained on single

us on a single as we had no anomalies detected so no problem as you

would expect and then on doubles uh well with the current system whatever limitations it

has to be certainly detected some anomalies

many where undetected uh not many but is more number of false positives and then

you associate the anomalies with the and you have a cold where they happen

they identified that reminds so it was very nice and that was very easy then

use that association and we have another paper elsewhere uh which uh

and then takes the output of this uh of this module of this anomaly detection

module and through this association is able to but what define the rule based basically

say well the court remove the animal is the cold size has to change and

it has to use that reminds us to uh to be able to in that

discontent successfully so you know eight

i think i

talked about i'll give you examples of all the mechanisms that the rainy day for

anomaly detection and but exercise by application uh principle you need this context detection which

is about domain detection a rather than a uh real or complex uh for system

to acquire new competence and once it has then it has to be able to

pick out which uh domain it's to do it but in a bit and that

the take the appropriate knowledge base and uh this is the basic system is used

in the interpretation that way role of a high level and the this is the

anomaly detection mechanism but that's the module that uh S is still need it and

that would be added to the system to

lexus successfully so that brings me to conclusion i hope that i have a display

did you that uh i know what detection in machine perception requires more mechanism then

what is normally what is just over the body conventional model and the and what

these mechanisms are and how useful in practical applications thank you very much attention

yeah

the use a system

well i think the you know what goes into the anomaly detection system i think

it's genetic about the application was specific okay so obviously are solutions will not work

for your problem about the i think of one uh the notion of data quality

is very important and the also the approach the problem that one needs one should

be trying to train the system just with the normandy time but it's you all

you also mulch within yourself in the foot because if you have examples of on

the money then it would help you to improve the design of the nevertheless uh

you know system then it will be able just to detect the what you presented

to it you in training and uh and so there is a little bit of

a dynamo yeah

i

okay uh

basically i think in all the protocol that all the videos that the use of

uh from professional matches and the cameras with fixed but any okay this is why

we needed to do the most like uh detection with section um

in principle

at least we always use the prior information that this the ground plane so you

need to based on the information you can solve a calibrate the comment on expect

to the scene of the speech and uh so uh you doesn't have to it

can move in it is not the solution is not just for a single position

of the common uh you can always uh contrary the system for any position and

this is what actually happens when a remote uses them

yeah

i think it was more to do with access uh i think the video speech

we go around the through internet ordering to internet maybe unique go but we didn't

looking into it uh but we knew that uh it would be difficult to get

the copies of the same but on broadcast

although we have a one of two with that B C so

a game and uh yeah

it that uh it's not regulate and i think that would say that we have

maybe they are losing probably for the confidence measure we are probably losing half of

the ten timeline

uh the way we are not making decisions because uh

the ambiguity and i'm because we can accuracy of the system and it actually gets

less is for the part of the core okay because the further away from the

comment often a degraded in accuracy

information

well i'm i hope it will generate some other one is but uh that's an

interesting proposition

thus

for

which

and